A rapid approach for discriminating <i>Ganoderma</i> species using attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometric analysis and convolutional neural network (CNN).

ABSTRACT: The issue of adulteration and misclassification of Ganoderma species is addressed in this research. In the study, we present a novel and comprehensive framework for Ganoderma authentication by analyzing attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectra using a combined approach of a chemometric analysis and deep learning (DL) with a convolutional neural network (CNN). The three Ganoderma species involved in this study were as follows: Ganoderma lucidum, Ganoderma sinense, and Ganoderma tsugae. Among chemometric models, orthogonal partial least squares discriminant analysis (OPLS-DA) yielded a high accuracy of 98.61%, a sensitivity of 97.92%, and a specificity of 98.96%. Additionally, the root-mean-squared error of estimatio

SUBMITTER: Chen SY 

PROVIDER: S-EPMC12597922 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature